Dimensionality reduction
Exercices
1. Load the nutrimouse data from the
mixOmics R package and investigate its structure.
library(mixOmics)A data object provided by an R package can be loaded with
data. Its structure can be obainted with str,
length, dim, etc.
data("nutrimouse")
## display the structure of the nutrimouse object
str(nutrimouse)## List of 4
## $ gene :'data.frame': 40 obs. of 120 variables:
## ..$ X36b4 : num [1:40] -0.42 -0.44 -0.48 -0.45 -0.42 -0.43 -0.53 -0.49 -0.36 -0.5 ...
## ..$ ACAT1 : num [1:40] -0.65 -0.68 -0.74 -0.69 -0.71 -0.69 -0.62 -0.69 -0.66 -0.62 ...
## ..$ ACAT2 : num [1:40] -0.84 -0.91 -1.1 -0.65 -0.54 -0.8 -1 -0.91 -0.74 -0.79 ...
## ..$ ACBP : num [1:40] -0.34 -0.32 -0.46 -0.41 -0.38 -0.32 -0.44 -0.37 -0.39 -0.36 ...
## ..$ ACC1 : num [1:40] -1.29 -1.23 -1.3 -1.26 -1.21 -1.13 -1.22 -1.29 -1.15 -1.21 ...
## ..$ ACC2 : num [1:40] -1.13 -1.06 -1.09 -1.09 -0.89 -0.79 -1 -1.06 -1.08 -0.82 ...
## ..$ ACOTH : num [1:40] -0.93 -0.99 -1.06 -0.93 -1 -0.93 -0.94 -1.05 -0.88 -0.92 ...
## ..$ ADISP : num [1:40] -0.98 -0.97 -1.08 -1.02 -0.95 -0.97 -0.94 -1.02 -0.98 -0.99 ...
## ..$ ADSS1 : num [1:40] -1.19 -1 -1.18 -1.07 -1.08 -1.07 -1.05 -1.16 -1.05 -1 ...
## ..$ ALDH3 : num [1:40] -0.68 -0.62 -0.75 -0.71 -0.76 -0.75 -0.67 -0.75 -0.66 -0.69 ...
## ..$ AM2R : num [1:40] -0.59 -0.58 -0.66 -0.65 -0.59 -0.55 -0.66 -0.66 -0.53 -0.62 ...
## ..$ AOX : num [1:40] -0.16 -0.12 -0.16 -0.17 -0.31 -0.23 -0.09 -0.22 -0.06 -0.23 ...
## ..$ BACT : num [1:40] -0.22 -0.32 -0.32 -0.32 -0.31 -0.29 -0.25 -0.21 -0.15 -0.2 ...
## ..$ BIEN : num [1:40] -0.89 -0.88 -0.89 -0.77 -0.97 -0.84 -0.86 -0.9 -0.74 -0.76 ...
## ..$ BSEP : num [1:40] -0.69 -0.6 -0.7 -0.67 -0.68 -0.55 -0.67 -0.66 -0.6 -0.58 ...
## ..$ Bcl.3 : num [1:40] -1.18 -1.07 -1.17 -1.12 -0.93 -1.08 -1.03 -1.01 -1.01 -1.1 ...
## ..$ C16SR : num [1:40] 1.66 1.65 1.57 1.61 1.66 1.7 1.58 1.62 1.72 1.55 ...
## ..$ CACP : num [1:40] -0.92 -0.87 -1.02 -0.89 -0.93 -0.97 -0.97 -0.96 -0.85 -0.95 ...
## ..$ CAR1 : num [1:40] -0.97 -0.92 -0.98 -0.97 -1.06 -1.03 -0.91 -1.11 -0.85 -0.99 ...
## ..$ CBS : num [1:40] -0.26 -0.36 -0.4 -0.39 -0.35 -0.31 -0.32 -0.4 -0.26 -0.39 ...
## ..$ CIDEA : num [1:40] -1.21 -1.17 -1.29 -1.18 -1.15 -1.14 -1.16 -1.26 -1.12 -1.08 ...
## ..$ COX1 : num [1:40] -1.11 -1.06 -1.17 -1.03 -0.99 -1.03 -1.15 -1.18 -0.94 -1.07 ...
## ..$ COX2 : num [1:40] -1.18 -1.06 -1.14 -1.13 -1.1 -1.16 -1.06 -1.24 -1.23 -1.09 ...
## ..$ CPT2 : num [1:40] -0.87 -0.87 -0.95 -0.88 -0.91 -0.92 -0.86 -0.93 -0.82 -0.88 ...
## ..$ CYP24 : num [1:40] -1.37 -1.14 -1.3 -1.27 -1.2 -1.11 -1.12 -1.3 -1.14 -1.08 ...
## ..$ CYP26 : num [1:40] -1.21 -1.12 -1.22 -1.18 -1.16 -1.1 -1.07 -1.23 -1.1 -1.1 ...
## ..$ CYP27a1 : num [1:40] -0.71 -0.62 -0.78 -0.71 -0.69 -0.6 -0.69 -0.81 -0.62 -0.62 ...
## ..$ CYP27b1 : num [1:40] -1.31 -1.14 -1.29 -1.27 -1.2 -1.15 -1.17 -1.28 -1.13 -1.15 ...
## ..$ CYP2b10 : num [1:40] -1.23 -1.2 -1.32 -1.23 -1.22 -1.1 -1.07 -1.26 -1.19 -1.1 ...
## ..$ CYP2b13 : num [1:40] -1.19 -1.06 -1.25 -1.13 -1.1 -1.07 -1.2 -1.37 -1.15 -1.11 ...
## ..$ CYP2c29 : num [1:40] -0.06 -0.2 -0.3 -0.07 -0.29 -0.28 -0.1 -0.1 0.18 -0.33 ...
## ..$ CYP3A11 : num [1:40] -0.09 -0.34 -0.45 -0.11 -0.51 -0.55 -0.18 -0.25 0.06 -0.4 ...
## ..$ CYP4A10 : num [1:40] -0.81 -0.88 -0.71 -0.65 -1.16 -0.99 -0.62 -0.82 -0.48 -0.79 ...
## ..$ CYP4A14 : num [1:40] -0.81 -0.84 -0.98 -0.41 -1.16 -1.09 -0.76 -0.87 -0.37 -0.95 ...
## ..$ CYP7a : num [1:40] -0.77 -0.71 -0.93 -0.8 -0.71 -0.74 -0.76 -0.88 -0.77 -0.77 ...
## ..$ CYP8b1 : num [1:40] -0.77 -0.63 -0.53 -0.73 -0.51 -0.55 -0.57 -0.63 -0.6 -0.66 ...
## ..$ FAS : num [1:40] -0.41 -0.37 -0.3 -0.59 -0.06 0.18 -0.16 0.04 -0.53 0.08 ...
## ..$ FAT : num [1:40] -1.03 -0.98 -1.03 -1.06 -0.99 -0.99 -0.89 -1.08 -1.04 -0.91 ...
## ..$ FDFT : num [1:40] -0.98 -0.92 -1.04 -1 -0.99 -1 -1.02 -0.97 -1.03 -0.95 ...
## ..$ FXR : num [1:40] -0.93 -0.87 -1 -0.9 -0.89 -0.89 -0.86 -1.01 -0.81 -0.91 ...
## ..$ G6PDH : num [1:40] -1.22 -1.09 -1.28 -1.19 -1.16 -0.96 -1.15 -1.26 -1.13 -1.03 ...
## ..$ G6Pase : num [1:40] -0.46 -0.63 -1.06 -0.71 -0.58 -0.49 -0.51 -0.61 -0.38 -0.6 ...
## ..$ GK : num [1:40] -0.71 -0.67 -0.68 -0.75 -0.62 -0.59 -0.59 -0.66 -0.68 -0.47 ...
## ..$ GS : num [1:40] -1.24 -1.22 -1.36 -1.21 -1.22 -1.16 -1.15 -1.31 -1.16 -1.19 ...
## ..$ GSTa : num [1:40] 0 -0.05 -0.13 -0.09 -0.02 -0.11 -0.06 -0.04 0.03 -0.02 ...
## ..$ GSTmu : num [1:40] 0.02 -0.05 -0.19 0.03 -0.23 -0.05 -0.22 -0.07 0.23 -0.14 ...
## ..$ GSTpi2 : num [1:40] 0.45 0.3 0.18 0.36 0.3 0.17 0.12 0.48 0.53 0.01 ...
## ..$ HMGCoAred: num [1:40] -0.95 -0.86 -0.96 -1.02 -0.7 -0.76 -1 -0.88 -0.96 -0.7 ...
## ..$ HPNCL : num [1:40] -0.65 -0.69 -0.75 -0.61 -0.66 -0.56 -0.61 -0.71 -0.53 -0.6 ...
## ..$ IL.2 : num [1:40] -0.94 -0.94 -1.16 -0.97 -0.93 -0.96 -0.96 -0.85 -0.84 -0.95 ...
## ..$ L.FABP : num [1:40] 0.24 0.27 0.17 0.16 0 0.23 0.18 0.18 0.2 0.2 ...
## ..$ LCE : num [1:40] 0.09 0.06 -0.05 0.01 -0.07 -0.1 -0.03 -0.08 0.12 -0.1 ...
## ..$ LDLr : num [1:40] -0.82 -0.68 -0.82 -0.94 -0.73 -0.74 -0.8 -0.83 -0.81 -0.72 ...
## ..$ LPK : num [1:40] -0.32 -0.39 -0.38 -0.38 -0.17 -0.14 -0.35 -0.13 -0.32 -0.24 ...
## ..$ LPL : num [1:40] -1.01 -0.97 -1.11 -0.99 -1.05 -0.99 -0.93 -1.07 -0.94 -0.95 ...
## ..$ LXRa : num [1:40] -0.82 -0.82 -0.91 -0.85 -0.83 -0.79 -0.77 -0.84 -0.75 -0.78 ...
## ..$ LXRb : num [1:40] -1 -0.95 -1.16 -1.01 -1.01 -0.99 -0.98 -1.04 -0.98 -0.99 ...
## ..$ Lpin : num [1:40] -0.87 -0.97 -0.95 -1 -0.57 -0.51 -0.81 -0.83 -0.83 -0.48 ...
## ..$ Lpin1 : num [1:40] -0.85 -0.99 -0.94 -1.02 -0.53 -0.51 -0.81 -0.87 -0.82 -0.49 ...
## ..$ Lpin2 : num [1:40] -0.85 -0.87 -0.9 -0.88 -0.72 -0.68 -0.8 -0.9 -0.68 -0.67 ...
## ..$ Lpin3 : num [1:40] -1.23 -1.12 -1.25 -1.18 -1.12 -1.09 -1.04 -1.23 -1.13 -1.11 ...
## ..$ M.CPT1 : num [1:40] -1.15 -1.06 -1.26 -1.1 -1.11 -1.14 -1.08 -1.19 -1.06 -1.09 ...
## ..$ MCAD : num [1:40] -0.6 -0.62 -0.7 -0.59 -0.69 -0.66 -0.53 -0.66 -0.45 -0.62 ...
## ..$ MDR1 : num [1:40] -1.15 -1.1 -1.26 -1.13 -1.11 -1.09 -1.09 -1.19 -1.06 -1.1 ...
## ..$ MDR2 : num [1:40] -0.77 -0.65 -0.86 -0.77 -0.7 -0.69 -0.81 -0.81 -0.69 -0.75 ...
## ..$ MRP6 : num [1:40] -0.99 -0.85 -0.9 -0.95 -0.91 -0.84 -0.88 -1.02 -0.83 -0.86 ...
## ..$ MS : num [1:40] -1.11 -1.06 -1.2 -1.09 -1.09 -1.09 -0.99 -1.16 -1.06 -0.98 ...
## ..$ MTHFR : num [1:40] -0.96 -0.99 -1.1 -0.95 -0.93 -0.96 -0.88 -1.03 -1.01 -0.95 ...
## ..$ NGFiB : num [1:40] -1.21 -1.08 -1.24 -1.12 -1.11 -1.04 -1.02 -1.21 -1.11 -1.04 ...
## ..$ NURR1 : num [1:40] -1.21 -1.1 -1.32 -1.11 -1.14 -1.18 -1.1 -1.26 -1.14 -1.09 ...
## ..$ Ntcp : num [1:40] -0.49 -0.45 -0.44 -0.54 -0.47 -0.46 -0.55 -0.5 -0.44 -0.43 ...
## ..$ OCTN2 : num [1:40] -1.15 -1.15 -1.2 -1.17 -1.19 -1.11 -1.08 -1.21 -1.05 -1.08 ...
## ..$ PAL : num [1:40] -1.32 -1.25 -1.16 -1.25 -1.24 -1.02 -1.04 -1.27 -0.93 -0.92 ...
## ..$ PDK4 : num [1:40] -1.16 -1.16 -1.27 -1.16 -1.13 -1.08 -1.14 -1.24 -1.19 -1.04 ...
## ..$ PECI : num [1:40] -0.68 -0.69 -0.92 -0.71 -0.83 -0.81 -0.79 -0.85 -0.58 -0.82 ...
## ..$ PLTP : num [1:40] -1.1 -0.99 -1.03 -1.08 -0.98 -0.89 -1.05 -1.07 -1.02 -0.85 ...
## ..$ PMDCI : num [1:40] -0.52 -0.52 -0.6 -0.52 -0.71 -0.69 -0.55 -0.57 -0.46 -0.69 ...
## ..$ PON : num [1:40] -0.52 -0.55 -0.65 -0.64 -0.57 -0.63 -0.56 -0.65 -0.6 -0.64 ...
## ..$ PPARa : num [1:40] -0.93 -0.86 -0.95 -0.97 -0.94 -0.95 -0.9 -1.12 -0.88 -0.95 ...
## ..$ PPARd : num [1:40] -1.51 -1.59 -1.71 -1.57 -1.53 -1.56 -1.49 -1.57 -1.58 -1.54 ...
## ..$ PPARg : num [1:40] -1.06 -1.02 -1.14 -1.05 -1.09 -1.01 -1 -1.13 -0.97 -1.07 ...
## ..$ PXR : num [1:40] -0.99 -0.96 -1.1 -0.99 -1 -1.03 -0.93 -1.07 -0.98 -0.96 ...
## ..$ Pex11a : num [1:40] -1 -1.02 -1.2 -1 -0.95 -1.07 -1.05 -1.02 -1 -1.01 ...
## ..$ RARa : num [1:40] -1.2 -1.06 -1.16 -1.17 -1.15 -1.13 -1.09 -1.24 -1.03 -1.09 ...
## ..$ RARb2 : num [1:40] -1.19 -1.11 -1.23 -1.16 -1.14 -1.07 -1.09 -1.18 -1.12 -1.1 ...
## ..$ RXRa : num [1:40] -0.67 -0.59 -0.68 -0.72 -0.78 -0.62 -0.65 -0.76 -0.55 -0.67 ...
## ..$ RXRb2 : num [1:40] -0.95 -0.95 -1.07 -0.95 -0.98 -0.94 -0.92 -1.03 -0.94 -0.95 ...
## ..$ RXRg1 : num [1:40] -1.16 -1.1 -1.21 -1.1 -1.11 -1.03 -1.07 -1.19 -1.05 -1.04 ...
## ..$ S14 : num [1:40] -0.93 -0.86 -0.84 -1.05 -0.65 -0.4 -0.73 -0.62 -0.99 -0.25 ...
## ..$ SHP1 : num [1:40] -1.1 -0.97 -1.09 -1.03 -1.13 -0.98 -0.95 -1.21 -0.93 -0.97 ...
## ..$ SIAT4c : num [1:40] -1.07 -0.97 -1.04 -0.99 -0.94 -0.93 -0.89 -1.04 -0.93 -0.95 ...
## ..$ SPI1.1 : num [1:40] 1.19 1.15 1.09 1.07 1.22 1.05 1.15 1.18 1.21 1.04 ...
## ..$ SR.BI : num [1:40] -0.84 -0.86 -0.95 -0.95 -1.06 -0.8 -0.83 -1 -0.83 -0.77 ...
## ..$ THB : num [1:40] -0.79 -0.85 -0.92 -0.79 -0.84 -0.86 -0.8 -0.86 -0.83 -0.85 ...
## ..$ THIOL : num [1:40] -0.18 -0.15 -0.24 -0.15 -0.35 -0.29 -0.22 -0.23 -0.17 -0.18 ...
## ..$ TRa : num [1:40] -1.48 -1.46 -1.58 -1.54 -1.46 -1.44 -1.32 -1.56 -1.46 -1.35 ...
## ..$ TRb : num [1:40] -1.07 -1 -1.16 -1.11 -1.01 -1 -0.97 -1.08 -1.02 -0.98 ...
## ..$ Tpalpha : num [1:40] -0.69 -0.74 -0.81 -0.74 -0.82 -0.76 -0.72 -0.76 -0.65 -0.83 ...
## ..$ Tpbeta : num [1:40] -1.11 -1.09 -1.14 -1.04 -1.2 -1.05 -1 -1.16 -0.91 -1.07 ...
## .. [list output truncated]
## $ lipid :'data.frame': 40 obs. of 21 variables:
## ..$ C14.0 : num [1:40] 0.34 0.38 0.36 0.22 0.37 1.7 0.35 0.34 0.22 1.38 ...
## ..$ C16.0 : num [1:40] 26.4 24 23.7 25.5 24.8 ...
## ..$ C18.0 : num [1:40] 10.22 9.93 8.96 8.14 9.63 ...
## ..$ C16.1n.9: num [1:40] 0.35 0.55 0.55 0.49 0.46 0.66 0.36 0.29 0.44 0.9 ...
## ..$ C16.1n.7: num [1:40] 3.1 2.54 2.65 2.82 2.85 7.26 3.6 3.27 2.36 7.01 ...
## ..$ C18.1n.9: num [1:40] 17 20.1 22.9 21.9 21.4 ...
## ..$ C18.1n.7: num [1:40] 2.41 3.92 3.96 2.52 2.96 8.99 2.15 1.99 1.81 8.85 ...
## ..$ C20.1n.9: num [1:40] 0.26 0.23 0.26 0 0.3 0.36 0.25 0.31 0 0.21 ...
## ..$ C20.3n.9: num [1:40] 0 0 0.19 0 0.27 2.89 0 0 0 2.03 ...
## ..$ C18.2n.6: num [1:40] 8.93 14.98 16.06 13.89 14.55 ...
## ..$ C18.3n.6: num [1:40] 0 0.3 0.27 0 0.27 2.66 0 0 0 0 ...
## ..$ C20.2n.6: num [1:40] 0 0.3 0.33 0 0.23 0 0 0 0 0 ...
## ..$ C20.3n.6: num [1:40] 0.78 1.64 1.51 1.1 1.58 0.81 0.68 0.72 1.07 0.59 ...
## ..$ C20.4n.6: num [1:40] 3.07 15.34 13.27 3.92 11.85 ...
## ..$ C22.4n.6: num [1:40] 0 0.58 0.54 0 0.32 0 0 0 0 0 ...
## ..$ C22.5n.6: num [1:40] 0 2.1 1.77 0 0.44 0.56 0 0 0 0.39 ...
## ..$ C18.3n.3: num [1:40] 5.97 0 0 0.49 0.42 0 8.4 6.01 0.55 0 ...
## ..$ C20.3n.3: num [1:40] 0.37 0 0 0 0 0 0.42 0.39 0 0 ...
## ..$ C20.5n.3: num [1:40] 8.62 0 0 2.99 0.3 0 7.37 7.96 3.13 0 ...
## ..$ C22.5n.3: num [1:40] 1.75 0.48 0.22 1.04 0.35 2.13 2.05 2.33 1.65 0 ...
## ..$ C22.6n.3: num [1:40] 10.39 2.61 2.51 14.99 6.69 ...
## $ diet : Factor w/ 5 levels "coc","fish","lin",..: 3 5 5 2 4 1 3 3 2 1 ...
## $ genotype: Factor w/ 2 levels "wt","ppar": 1 1 1 1 1 1 1 1 1 1 ...## check dimensions
lapply(nutrimouse, dim) # apply function dim to each element in list nutrimouse## $gene
## [1] 40 120
##
## $lipid
## [1] 40 21
##
## $diet
## NULL
##
## $genotype
## NULLlapply(nutrimouse, length) # apply function length to each element in list nutrimouse## $gene
## [1] 120
##
## $lipid
## [1] 21
##
## $diet
## [1] 40
##
## $genotype
## [1] 402. Take the gene expression dataset in samples x variables matrix format. Investigate their distribution.
## get gene expression data structure
str(nutrimouse$gene)## 'data.frame': 40 obs. of 120 variables:
## $ X36b4 : num -0.42 -0.44 -0.48 -0.45 -0.42 -0.43 -0.53 -0.49 -0.36 -0.5 ...
## $ ACAT1 : num -0.65 -0.68 -0.74 -0.69 -0.71 -0.69 -0.62 -0.69 -0.66 -0.62 ...
## $ ACAT2 : num -0.84 -0.91 -1.1 -0.65 -0.54 -0.8 -1 -0.91 -0.74 -0.79 ...
## $ ACBP : num -0.34 -0.32 -0.46 -0.41 -0.38 -0.32 -0.44 -0.37 -0.39 -0.36 ...
## $ ACC1 : num -1.29 -1.23 -1.3 -1.26 -1.21 -1.13 -1.22 -1.29 -1.15 -1.21 ...
## $ ACC2 : num -1.13 -1.06 -1.09 -1.09 -0.89 -0.79 -1 -1.06 -1.08 -0.82 ...
## $ ACOTH : num -0.93 -0.99 -1.06 -0.93 -1 -0.93 -0.94 -1.05 -0.88 -0.92 ...
## $ ADISP : num -0.98 -0.97 -1.08 -1.02 -0.95 -0.97 -0.94 -1.02 -0.98 -0.99 ...
## $ ADSS1 : num -1.19 -1 -1.18 -1.07 -1.08 -1.07 -1.05 -1.16 -1.05 -1 ...
## $ ALDH3 : num -0.68 -0.62 -0.75 -0.71 -0.76 -0.75 -0.67 -0.75 -0.66 -0.69 ...
## $ AM2R : num -0.59 -0.58 -0.66 -0.65 -0.59 -0.55 -0.66 -0.66 -0.53 -0.62 ...
## $ AOX : num -0.16 -0.12 -0.16 -0.17 -0.31 -0.23 -0.09 -0.22 -0.06 -0.23 ...
## $ BACT : num -0.22 -0.32 -0.32 -0.32 -0.31 -0.29 -0.25 -0.21 -0.15 -0.2 ...
## $ BIEN : num -0.89 -0.88 -0.89 -0.77 -0.97 -0.84 -0.86 -0.9 -0.74 -0.76 ...
## $ BSEP : num -0.69 -0.6 -0.7 -0.67 -0.68 -0.55 -0.67 -0.66 -0.6 -0.58 ...
## $ Bcl.3 : num -1.18 -1.07 -1.17 -1.12 -0.93 -1.08 -1.03 -1.01 -1.01 -1.1 ...
## $ C16SR : num 1.66 1.65 1.57 1.61 1.66 1.7 1.58 1.62 1.72 1.55 ...
## $ CACP : num -0.92 -0.87 -1.02 -0.89 -0.93 -0.97 -0.97 -0.96 -0.85 -0.95 ...
## $ CAR1 : num -0.97 -0.92 -0.98 -0.97 -1.06 -1.03 -0.91 -1.11 -0.85 -0.99 ...
## $ CBS : num -0.26 -0.36 -0.4 -0.39 -0.35 -0.31 -0.32 -0.4 -0.26 -0.39 ...
## $ CIDEA : num -1.21 -1.17 -1.29 -1.18 -1.15 -1.14 -1.16 -1.26 -1.12 -1.08 ...
## $ COX1 : num -1.11 -1.06 -1.17 -1.03 -0.99 -1.03 -1.15 -1.18 -0.94 -1.07 ...
## $ COX2 : num -1.18 -1.06 -1.14 -1.13 -1.1 -1.16 -1.06 -1.24 -1.23 -1.09 ...
## $ CPT2 : num -0.87 -0.87 -0.95 -0.88 -0.91 -0.92 -0.86 -0.93 -0.82 -0.88 ...
## $ CYP24 : num -1.37 -1.14 -1.3 -1.27 -1.2 -1.11 -1.12 -1.3 -1.14 -1.08 ...
## $ CYP26 : num -1.21 -1.12 -1.22 -1.18 -1.16 -1.1 -1.07 -1.23 -1.1 -1.1 ...
## $ CYP27a1 : num -0.71 -0.62 -0.78 -0.71 -0.69 -0.6 -0.69 -0.81 -0.62 -0.62 ...
## $ CYP27b1 : num -1.31 -1.14 -1.29 -1.27 -1.2 -1.15 -1.17 -1.28 -1.13 -1.15 ...
## $ CYP2b10 : num -1.23 -1.2 -1.32 -1.23 -1.22 -1.1 -1.07 -1.26 -1.19 -1.1 ...
## $ CYP2b13 : num -1.19 -1.06 -1.25 -1.13 -1.1 -1.07 -1.2 -1.37 -1.15 -1.11 ...
## $ CYP2c29 : num -0.06 -0.2 -0.3 -0.07 -0.29 -0.28 -0.1 -0.1 0.18 -0.33 ...
## $ CYP3A11 : num -0.09 -0.34 -0.45 -0.11 -0.51 -0.55 -0.18 -0.25 0.06 -0.4 ...
## $ CYP4A10 : num -0.81 -0.88 -0.71 -0.65 -1.16 -0.99 -0.62 -0.82 -0.48 -0.79 ...
## $ CYP4A14 : num -0.81 -0.84 -0.98 -0.41 -1.16 -1.09 -0.76 -0.87 -0.37 -0.95 ...
## $ CYP7a : num -0.77 -0.71 -0.93 -0.8 -0.71 -0.74 -0.76 -0.88 -0.77 -0.77 ...
## $ CYP8b1 : num -0.77 -0.63 -0.53 -0.73 -0.51 -0.55 -0.57 -0.63 -0.6 -0.66 ...
## $ FAS : num -0.41 -0.37 -0.3 -0.59 -0.06 0.18 -0.16 0.04 -0.53 0.08 ...
## $ FAT : num -1.03 -0.98 -1.03 -1.06 -0.99 -0.99 -0.89 -1.08 -1.04 -0.91 ...
## $ FDFT : num -0.98 -0.92 -1.04 -1 -0.99 -1 -1.02 -0.97 -1.03 -0.95 ...
## $ FXR : num -0.93 -0.87 -1 -0.9 -0.89 -0.89 -0.86 -1.01 -0.81 -0.91 ...
## $ G6PDH : num -1.22 -1.09 -1.28 -1.19 -1.16 -0.96 -1.15 -1.26 -1.13 -1.03 ...
## $ G6Pase : num -0.46 -0.63 -1.06 -0.71 -0.58 -0.49 -0.51 -0.61 -0.38 -0.6 ...
## $ GK : num -0.71 -0.67 -0.68 -0.75 -0.62 -0.59 -0.59 -0.66 -0.68 -0.47 ...
## $ GS : num -1.24 -1.22 -1.36 -1.21 -1.22 -1.16 -1.15 -1.31 -1.16 -1.19 ...
## $ GSTa : num 0 -0.05 -0.13 -0.09 -0.02 -0.11 -0.06 -0.04 0.03 -0.02 ...
## $ GSTmu : num 0.02 -0.05 -0.19 0.03 -0.23 -0.05 -0.22 -0.07 0.23 -0.14 ...
## $ GSTpi2 : num 0.45 0.3 0.18 0.36 0.3 0.17 0.12 0.48 0.53 0.01 ...
## $ HMGCoAred: num -0.95 -0.86 -0.96 -1.02 -0.7 -0.76 -1 -0.88 -0.96 -0.7 ...
## $ HPNCL : num -0.65 -0.69 -0.75 -0.61 -0.66 -0.56 -0.61 -0.71 -0.53 -0.6 ...
## $ IL.2 : num -0.94 -0.94 -1.16 -0.97 -0.93 -0.96 -0.96 -0.85 -0.84 -0.95 ...
## $ L.FABP : num 0.24 0.27 0.17 0.16 0 0.23 0.18 0.18 0.2 0.2 ...
## $ LCE : num 0.09 0.06 -0.05 0.01 -0.07 -0.1 -0.03 -0.08 0.12 -0.1 ...
## $ LDLr : num -0.82 -0.68 -0.82 -0.94 -0.73 -0.74 -0.8 -0.83 -0.81 -0.72 ...
## $ LPK : num -0.32 -0.39 -0.38 -0.38 -0.17 -0.14 -0.35 -0.13 -0.32 -0.24 ...
## $ LPL : num -1.01 -0.97 -1.11 -0.99 -1.05 -0.99 -0.93 -1.07 -0.94 -0.95 ...
## $ LXRa : num -0.82 -0.82 -0.91 -0.85 -0.83 -0.79 -0.77 -0.84 -0.75 -0.78 ...
## $ LXRb : num -1 -0.95 -1.16 -1.01 -1.01 -0.99 -0.98 -1.04 -0.98 -0.99 ...
## $ Lpin : num -0.87 -0.97 -0.95 -1 -0.57 -0.51 -0.81 -0.83 -0.83 -0.48 ...
## $ Lpin1 : num -0.85 -0.99 -0.94 -1.02 -0.53 -0.51 -0.81 -0.87 -0.82 -0.49 ...
## $ Lpin2 : num -0.85 -0.87 -0.9 -0.88 -0.72 -0.68 -0.8 -0.9 -0.68 -0.67 ...
## $ Lpin3 : num -1.23 -1.12 -1.25 -1.18 -1.12 -1.09 -1.04 -1.23 -1.13 -1.11 ...
## $ M.CPT1 : num -1.15 -1.06 -1.26 -1.1 -1.11 -1.14 -1.08 -1.19 -1.06 -1.09 ...
## $ MCAD : num -0.6 -0.62 -0.7 -0.59 -0.69 -0.66 -0.53 -0.66 -0.45 -0.62 ...
## $ MDR1 : num -1.15 -1.1 -1.26 -1.13 -1.11 -1.09 -1.09 -1.19 -1.06 -1.1 ...
## $ MDR2 : num -0.77 -0.65 -0.86 -0.77 -0.7 -0.69 -0.81 -0.81 -0.69 -0.75 ...
## $ MRP6 : num -0.99 -0.85 -0.9 -0.95 -0.91 -0.84 -0.88 -1.02 -0.83 -0.86 ...
## $ MS : num -1.11 -1.06 -1.2 -1.09 -1.09 -1.09 -0.99 -1.16 -1.06 -0.98 ...
## $ MTHFR : num -0.96 -0.99 -1.1 -0.95 -0.93 -0.96 -0.88 -1.03 -1.01 -0.95 ...
## $ NGFiB : num -1.21 -1.08 -1.24 -1.12 -1.11 -1.04 -1.02 -1.21 -1.11 -1.04 ...
## $ NURR1 : num -1.21 -1.1 -1.32 -1.11 -1.14 -1.18 -1.1 -1.26 -1.14 -1.09 ...
## $ Ntcp : num -0.49 -0.45 -0.44 -0.54 -0.47 -0.46 -0.55 -0.5 -0.44 -0.43 ...
## $ OCTN2 : num -1.15 -1.15 -1.2 -1.17 -1.19 -1.11 -1.08 -1.21 -1.05 -1.08 ...
## $ PAL : num -1.32 -1.25 -1.16 -1.25 -1.24 -1.02 -1.04 -1.27 -0.93 -0.92 ...
## $ PDK4 : num -1.16 -1.16 -1.27 -1.16 -1.13 -1.08 -1.14 -1.24 -1.19 -1.04 ...
## $ PECI : num -0.68 -0.69 -0.92 -0.71 -0.83 -0.81 -0.79 -0.85 -0.58 -0.82 ...
## $ PLTP : num -1.1 -0.99 -1.03 -1.08 -0.98 -0.89 -1.05 -1.07 -1.02 -0.85 ...
## $ PMDCI : num -0.52 -0.52 -0.6 -0.52 -0.71 -0.69 -0.55 -0.57 -0.46 -0.69 ...
## $ PON : num -0.52 -0.55 -0.65 -0.64 -0.57 -0.63 -0.56 -0.65 -0.6 -0.64 ...
## $ PPARa : num -0.93 -0.86 -0.95 -0.97 -0.94 -0.95 -0.9 -1.12 -0.88 -0.95 ...
## $ PPARd : num -1.51 -1.59 -1.71 -1.57 -1.53 -1.56 -1.49 -1.57 -1.58 -1.54 ...
## $ PPARg : num -1.06 -1.02 -1.14 -1.05 -1.09 -1.01 -1 -1.13 -0.97 -1.07 ...
## $ PXR : num -0.99 -0.96 -1.1 -0.99 -1 -1.03 -0.93 -1.07 -0.98 -0.96 ...
## $ Pex11a : num -1 -1.02 -1.2 -1 -0.95 -1.07 -1.05 -1.02 -1 -1.01 ...
## $ RARa : num -1.2 -1.06 -1.16 -1.17 -1.15 -1.13 -1.09 -1.24 -1.03 -1.09 ...
## $ RARb2 : num -1.19 -1.11 -1.23 -1.16 -1.14 -1.07 -1.09 -1.18 -1.12 -1.1 ...
## $ RXRa : num -0.67 -0.59 -0.68 -0.72 -0.78 -0.62 -0.65 -0.76 -0.55 -0.67 ...
## $ RXRb2 : num -0.95 -0.95 -1.07 -0.95 -0.98 -0.94 -0.92 -1.03 -0.94 -0.95 ...
## $ RXRg1 : num -1.16 -1.1 -1.21 -1.1 -1.11 -1.03 -1.07 -1.19 -1.05 -1.04 ...
## $ S14 : num -0.93 -0.86 -0.84 -1.05 -0.65 -0.4 -0.73 -0.62 -0.99 -0.25 ...
## $ SHP1 : num -1.1 -0.97 -1.09 -1.03 -1.13 -0.98 -0.95 -1.21 -0.93 -0.97 ...
## $ SIAT4c : num -1.07 -0.97 -1.04 -0.99 -0.94 -0.93 -0.89 -1.04 -0.93 -0.95 ...
## $ SPI1.1 : num 1.19 1.15 1.09 1.07 1.22 1.05 1.15 1.18 1.21 1.04 ...
## $ SR.BI : num -0.84 -0.86 -0.95 -0.95 -1.06 -0.8 -0.83 -1 -0.83 -0.77 ...
## $ THB : num -0.79 -0.85 -0.92 -0.79 -0.84 -0.86 -0.8 -0.86 -0.83 -0.85 ...
## $ THIOL : num -0.18 -0.15 -0.24 -0.15 -0.35 -0.29 -0.22 -0.23 -0.17 -0.18 ...
## $ TRa : num -1.48 -1.46 -1.58 -1.54 -1.46 -1.44 -1.32 -1.56 -1.46 -1.35 ...
## $ TRb : num -1.07 -1 -1.16 -1.11 -1.01 -1 -0.97 -1.08 -1.02 -0.98 ...
## $ Tpalpha : num -0.69 -0.74 -0.81 -0.74 -0.82 -0.76 -0.72 -0.76 -0.65 -0.83 ...
## $ Tpbeta : num -1.11 -1.09 -1.14 -1.04 -1.2 -1.05 -1 -1.16 -0.91 -1.07 ...
## [list output truncated]## check if there are missing values
any(is.na(nutrimouse$gene))## [1] FALSE## investigate each variable
summary(nutrimouse$gene[, 1])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.5800 -0.5025 -0.4600 -0.4552 -0.4200 -0.3000colors <- rainbow(20, alpha=1)
plot(density(scale(nutrimouse$gene[, 1], center=T, scale=F)),
col=colors[1], xlim=c(-0.5,0.5), ylim=c(0,8))
sapply(2:20, function(i) {
lines(density(scale(nutrimouse$gene[, i], center=T, scale=F)), col=colors[i])
})## [[1]]
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## NULLapply(nutrimouse$gene, 2, summary)## X36b4 ACAT1 ACAT2 ACBP ACC1 ACC2 ACOTH ADISP
## Min. -0.58000 -0.75000 -1.10000 -0.66000 -1.4400 -1.2000 -1.06000 -1.08000
## 1st Qu. -0.50250 -0.69000 -0.88000 -0.50250 -1.3000 -1.0900 -0.95000 -1.02000
## Median -0.46000 -0.66000 -0.79500 -0.42500 -1.2600 -1.0450 -0.92000 -0.97000
## Mean -0.45525 -0.65525 -0.76675 -0.43375 -1.2585 -1.0280 -0.91075 -0.97825
## 3rd Qu. -0.42000 -0.62000 -0.64500 -0.35500 -1.2200 -0.9875 -0.88000 -0.94000
## Max. -0.30000 -0.52000 -0.39000 -0.24000 -1.0700 -0.7900 -0.73000 -0.87000
## ADSS1 ALDH3 AM2R AOX BACT BIEN BSEP Bcl.3
## Min. -1.19000 -0.9900 -0.780 -0.4800 -0.44000 -1.16000 -0.9000 -1.22000
## 1st Qu. -1.14000 -0.9100 -0.670 -0.3175 -0.32250 -0.99000 -0.7600 -1.10250
## Median -1.07500 -0.7850 -0.630 -0.2300 -0.30000 -0.92000 -0.7000 -1.06500
## Mean -1.07575 -0.8100 -0.628 -0.2505 -0.28275 -0.92125 -0.6910 -1.05875
## 3rd Qu. -1.03500 -0.7475 -0.590 -0.1675 -0.23500 -0.85500 -0.6275 -1.01000
## Max. -0.91000 -0.6200 -0.460 -0.0400 -0.11000 -0.64000 -0.5100 -0.91000
## C16SR CACP CAR1 CBS CIDEA COX1 COX2 CPT2 CYP24
## Min. 1.55000 -1.2600 -1.1900 -0.5600 -1.3300 -1.18000 -1.280 -1.2000 -1.3700
## 1st Qu. 1.59000 -1.0325 -0.9900 -0.4450 -1.2325 -1.09250 -1.180 -1.0100 -1.2600
## Median 1.61000 -0.9800 -0.9100 -0.4000 -1.1700 -1.05500 -1.130 -0.9450 -1.1800
## Mean 1.62675 -0.9845 -0.9135 -0.3995 -1.1840 -1.04975 -1.135 -0.9565 -1.1925
## 3rd Qu. 1.65250 -0.9375 -0.8475 -0.3375 -1.1400 -1.01000 -1.090 -0.8800 -1.1375
## Max. 1.78000 -0.8300 -0.6300 -0.2600 -1.0700 -0.88000 -1.040 -0.8200 -1.0500
## CYP26 CYP27a1 CYP27b1 CYP2b10 CYP2b13 CYP2c29 CYP3A11 CYP4A10
## Min. -1.3200 -0.88000 -1.350 -1.32000 -1.37000 -0.52000 -1.02000 -1.33000
## 1st Qu. -1.2225 -0.78500 -1.245 -1.23000 -1.19250 -0.28250 -0.71250 -1.15250
## Median -1.1500 -0.73000 -1.180 -1.20000 -1.14000 -0.14000 -0.53000 -1.05000
## Mean -1.1560 -0.72725 -1.200 -1.18475 -1.14575 -0.14725 -0.50825 -0.97975
## 3rd Qu. -1.1000 -0.67000 -1.150 -1.15000 -1.09750 -0.03000 -0.38500 -0.81750
## Max. -0.9600 -0.59000 -0.990 -1.04000 -0.96000 0.18000 0.06000 -0.48000
## CYP4A14 CYP7a CYP8b1 FAS FAT FDFT FXR G6PDH
## Min. -1.2900 -0.9300 -1.01000 -1.05000 -1.0900 -1.17000 -1.0600 -1.30000
## 1st Qu. -1.1500 -0.8000 -0.76000 -0.67000 -1.0400 -1.02000 -0.9525 -1.20250
## Median -1.0800 -0.7700 -0.67000 -0.49000 -0.9950 -0.99000 -0.9000 -1.15000
## Mean -0.9930 -0.7695 -0.68225 -0.45175 -0.9910 -0.98075 -0.9105 -1.15125
## 3rd Qu. -0.8925 -0.7400 -0.59000 -0.22500 -0.9475 -0.93750 -0.8775 -1.10750
## Max. -0.1500 -0.6100 -0.50000 0.18000 -0.7500 -0.81000 -0.7600 -0.96000
## G6Pase GK GS GSTa GSTmu GSTpi2 HMGCoAred HPNCL
## Min. -1.06000 -0.9600 -1.3800 -0.4300 -0.440 0.00000 -1.0700 -0.97000
## 1st Qu. -0.82000 -0.8000 -1.3025 -0.1525 -0.200 0.12000 -0.9700 -0.75000
## Median -0.69000 -0.7000 -1.2250 -0.0900 -0.140 0.21000 -0.9300 -0.69000
## Mean -0.69825 -0.7145 -1.2325 -0.1030 -0.119 0.22975 -0.9135 -0.69375
## 3rd Qu. -0.53500 -0.6200 -1.1675 -0.0350 -0.050 0.33250 -0.8750 -0.60750
## Max. -0.38000 -0.4600 -1.1200 0.0400 0.230 0.55000 -0.7000 -0.53000
## IL.2 L.FABP LCE LDLr LPK LPL LXRa LXRb
## Min. -1.1600 -0.4600 -0.26000 -0.9600 -0.570 -1.11000 -0.9100 -1.1600
## 1st Qu. -1.0025 -0.0750 -0.10000 -0.8525 -0.395 -1.03000 -0.8400 -1.0225
## Median -0.9450 0.0600 -0.06000 -0.8200 -0.350 -0.99000 -0.8150 -0.9900
## Mean -0.9505 0.0340 -0.05275 -0.8195 -0.344 -0.99075 -0.8115 -0.9960
## 3rd Qu. -0.8975 0.1825 0.00000 -0.7675 -0.295 -0.95000 -0.7775 -0.9675
## Max. -0.8200 0.2800 0.12000 -0.6800 -0.130 -0.86000 -0.6500 -0.8400
## Lpin Lpin1 Lpin2 Lpin3 M.CPT1 MCAD MDR1 MDR2
## Min. -1.13000 -1.10000 -1.140 -1.2900 -1.29000 -0.7300 -1.30000 -0.92000
## 1st Qu. -0.85500 -0.87000 -0.910 -1.1975 -1.16500 -0.6600 -1.16250 -0.83000
## Median -0.72500 -0.76000 -0.855 -1.1450 -1.12000 -0.6200 -1.12000 -0.78000
## Mean -0.75325 -0.76475 -0.849 -1.1475 -1.12575 -0.6050 -1.13425 -0.77875
## 3rd Qu. -0.61500 -0.64000 -0.775 -1.0975 -1.09000 -0.5575 -1.09000 -0.71750
## Max. -0.48000 -0.49000 -0.670 -0.9800 -0.96000 -0.4200 -0.99000 -0.65000
## MRP6 MS MTHFR NGFiB NURR1 Ntcp OCTN2 PAL
## Min. -1.09000 -1.20000 -1.1000 -1.29000 -1.32000 -0.6500 -1.28000 -1.3200
## 1st Qu. -1.00250 -1.11000 -1.0025 -1.20000 -1.21000 -0.4925 -1.19000 -1.2550
## Median -0.95500 -1.06500 -0.9700 -1.12000 -1.14000 -0.4400 -1.15000 -1.2000
## Mean -0.94775 -1.06075 -0.9720 -1.12925 -1.16125 -0.4370 -1.13925 -1.1445
## 3rd Qu. -0.87750 -1.00750 -0.9300 -1.07750 -1.10750 -0.3675 -1.08000 -1.0075
## Max. -0.83000 -0.88000 -0.8800 -0.91000 -0.95000 -0.2500 -1.04000 -0.8900
## PDK4 PECI PLTP PMDCI PON PPARa PPARd PPARg
## Min. -1.28000 -1.11000 -1.15000 -1.07000 -0.7100 -1.1400 -1.7100 -1.190
## 1st Qu. -1.17250 -0.92250 -1.09250 -0.94250 -0.6325 -1.0225 -1.5900 -1.090
## Median -1.13000 -0.84000 -1.05000 -0.76500 -0.5800 -0.9500 -1.5600 -1.055
## Mean -1.13525 -0.84725 -1.03625 -0.76725 -0.5825 -0.9660 -1.5595 -1.052
## 3rd Qu. -1.08000 -0.79750 -0.99750 -0.60000 -0.5375 -0.9000 -1.5100 -1.010
## Max. -1.01000 -0.58000 -0.85000 -0.44000 -0.4500 -0.8300 -1.4300 -0.900
## PXR Pex11a RARa RARb2 RXRa RXRb2 RXRg1 S14
## Min. -1.13000 -1.2000 -1.30000 -1.3000 -0.7800 -1.070 -1.2300 -1.05000
## 1st Qu. -1.03000 -1.0500 -1.18250 -1.1900 -0.6725 -1.000 -1.1425 -0.98000
## Median -0.99000 -1.0200 -1.13000 -1.1350 -0.6350 -0.960 -1.1000 -0.85500
## Mean -0.99225 -1.0220 -1.13325 -1.1445 -0.6360 -0.964 -1.0955 -0.80675
## 3rd Qu. -0.94750 -0.9875 -1.07500 -1.0900 -0.5875 -0.935 -1.0500 -0.65750
## Max. -0.84000 -0.9000 -0.97000 -0.9900 -0.4900 -0.780 -0.9000 -0.25000
## SHP1 SIAT4c SPI1.1 SR.BI THB THIOL TRa TRb
## Min. -1.21000 -1.16000 0.96000 -1.060 -0.9200 -0.900 -1.670 -1.22000
## 1st Qu. -1.07500 -0.99000 1.03750 -0.920 -0.8500 -0.590 -1.510 -1.11000
## Median -0.99000 -0.96000 1.07500 -0.830 -0.8200 -0.345 -1.460 -1.06000
## Mean -1.00675 -0.96225 1.09075 -0.843 -0.8170 -0.411 -1.457 -1.05425
## 3rd Qu. -0.94750 -0.92750 1.15000 -0.800 -0.7875 -0.230 -1.395 -0.99750
## Max. -0.78000 -0.84000 1.23000 -0.610 -0.6900 -0.030 -1.220 -0.92000
## Tpalpha Tpbeta UCP2 UCP3 VDR VLDLr Waf1 ap2
## Min. -1.00000 -1.310 -1.0800 -1.27000 -1.30000 -1.19000 -1.3000 -1.3700
## 1st Qu. -0.86000 -1.200 -1.0025 -1.15250 -1.18000 -1.09250 -1.1500 -1.2225
## Median -0.83000 -1.140 -0.9800 -1.11000 -1.12000 -1.05500 -1.1300 -1.1900
## Mean -0.81825 -1.130 -0.9660 -1.10775 -1.13175 -1.05325 -1.1235 -1.1880
## 3rd Qu. -0.76000 -1.065 -0.9275 -1.05000 -1.08000 -1.01000 -1.0875 -1.1475
## Max. -0.65000 -0.910 -0.7600 -0.92000 -0.94000 -0.91000 -0.9400 -1.0800
## apoA.I apoB apoC3 apoE c.fos cHMGCoAS cMOAT eif2g
## Min. 0.5400 -0.2700 -0.49000 0.86000 -1.22000 -1.24000 -1.0200 -1.230
## 1st Qu. 0.6575 -0.2000 -0.39000 0.98000 -1.15000 -1.10250 -0.8950 -1.100
## Median 0.7200 -0.1700 -0.34000 1.04000 -1.11000 -1.03000 -0.8700 -1.055
## Mean 0.7295 -0.1675 -0.34075 1.02825 -1.10525 -1.01375 -0.8485 -1.058
## 3rd Qu. 0.8100 -0.1450 -0.30000 1.07000 -1.06000 -0.91000 -0.7875 -1.020
## Max. 0.9200 0.0100 -0.18000 1.18000 -0.98000 -0.78000 -0.6900 -0.840
## hABC1 i.BABP i.BAT i.FABP i.NOS mABC1 mHMGCoAS
## Min. -1.25000 -0.8900 -1.89000 -1.300 -1.4300 -0.9800 -0.5800
## 1st Qu. -1.17250 -0.8325 -1.74250 -1.170 -1.2850 -0.9200 -0.3000
## Median -1.13500 -0.8000 -1.69000 -1.140 -1.2400 -0.8700 -0.2100
## Mean -1.13825 -0.7935 -1.69775 -1.122 -1.2460 -0.8765 -0.2210
## 3rd Qu. -1.09750 -0.7475 -1.66000 -1.075 -1.2075 -0.8375 -0.1275
## Max. -0.98000 -0.6700 -1.55000 -0.930 -1.0900 -0.8000 0.0600
PLS
1. Perform PLS (mixOmics::pls) and investigate the
output, sample distribution and variable relationship with plots.
pls.res <- pls(X=nutrimouse$gene, Y=nutrimouse$lipid, ncomp=2, scale=TRUE, mode="canonical")
max(abs(scale(nutrimouse$gene, center=T, scale=T) - pls.res$X))## [1] 0max(abs(scale(nutrimouse$lipid, center=T, scale=T) - pls.res$Y))## [1] 0The sample distribution plot can be performed with variates, sample coordinates in the new reference (rotated axes) for each of the two blocks.
str(pls.res$variates)## List of 2
## $ X: num [1:40, 1:2] 6.659 0.614 9.876 4.864 0.934 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:40] "1" "2" "3" "4" ...
## .. ..$ : chr [1:2] "comp1" "comp2"
## $ Y: num [1:40, 1:2] 2.33 2.6 1.98 1.94 2.29 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:40] "1" "2" "3" "4" ...
## .. ..$ : chr [1:2] "comp1" "comp2"PCx <- "comp1"
PCy <- "comp2"
par(mfrow=c(1,2))
plot(pls.res$variates$X[, PCx], pls.res$variates$X[, PCy], xlab=PCx, ylab=PCy, main="X", type='n')
text(pls.res$variates$X[, PCx], pls.res$variates$X[, PCy], rownames(pls.res$variates$X), col='blue', cex=0.6)
plot(pls.res$variates$Y[, PCx], pls.res$variates$Y[, PCy], xlab=PCx, ylab=PCy, main="Y", type='n')
text(pls.res$variates$Y[, PCx], pls.res$variates$Y[, PCy], rownames(pls.res$variates$Y), col='blue', cex=0.6)
which is also produced with plotIndiv.
plotIndiv(pls.res)
Loading plot: plot of variables’ contribution in each data block to each variate, after deflating more important variates.
par(mfrow=c(1,2), las=2, mar=c(4,8,1,1))
loadings.ind.X <- order(abs(pls.res$loadings$X[, "comp1"]), decreasing = T)
barplot(head(pls.res$loadings$X[loadings.ind.X, "comp1"], 10), main="X", horiz = T, cex.names=0.8)
loadings.ind.Y <- order(abs(pls.res$loadings$Y[, "comp1"]), decreasing = T)
barplot(head(pls.res$loadings$Y[loadings.ind.Y, "comp1"], 10), main="Y", horiz = T, cex.names=0.8)
which is the same as with plotLoadings.
plotLoadings(pls.res, ndisplay = 10)
The plot of variable relationship could be obtained from loadings.star.
names(pls.res$loadings.star) <- c("X", "Y")
colnames(pls.res$loadings.star$X) <- colnames(pls.res$loadings.star$Y) <- c(PCx, PCy)
plot(1,1,type='n',
xlim=range(c(pls.res$loadings.star$X[, PCx],pls.res$loadings.star$Y[, PCx])),
ylim=range(c(pls.res$loadings.star$X[, PCy],pls.res$loadings.star$Y[, PCy])))
arrows(0, 0, pls.res$loadings.star$X[, PCx], pls.res$loadings.star$X[, PCy],
length=0.1, angle=20, col=rgb(0,0,1,alpha=apply(pls.res$loadings.star$X[, c(PCx, PCy)], 1, norm, "2")))
text(pls.res$loadings.star$X[, PCx],
pls.res$loadings.star$X[, PCy],
rownames(pls.res$loadings.star$X), col='grey', cex=0.7)
arrows(0, 0, pls.res$loadings.star$Y[, PCx], pls.res$loadings.star$Y[, PCy],
length=0.1, angle=20, col=rgb(1,0,0,alpha=apply(pls.res$loadings.star$Y[, c(PCx, PCy)], 1, norm, "2")))
text(pls.res$loadings.star$Y[, PCx],
pls.res$loadings.star$Y[, PCy],
rownames(pls.res$loadings.star$Y), col='grey', cex=0.7)
plotVar(pls.res)
Both sample distribution and variable relationship plot could be done
with biplot function.
biplot(pls.res, block="X", ind.names.size=3, var.names.size=2)
biplot(pls.res, block="Y", ind.names.size=3, var.names.size=2)
#### 2. Observe the difference between the two modes regression
and canonical of PLS.
pls.reg.res <- pls(X=nutrimouse$gene, Y=nutrimouse$lipid, ncomp=2, scale=TRUE, mode="regression")
biplot(pls.res, block="X", ind.names.size=3, var.names.size=2)
biplot(pls.res, block="Y", ind.names.size=3, var.names.size=2)
3. PLS between genes and genotype
pls.regda.res <- pls(X=nutrimouse$gene, Y=c(0,1)[nutrimouse$genotype], ncomp=2, scale=TRUE, mode="regression")
biplot(pls.regda.res, block="X", ind.names.size=3, var.names.size=2, group=nutrimouse$genotype, col.per.group = c("red", "blue"))